The Atlantic Forest Green Corridor in Misiones, Argentina, is one of the last remaining bastions of subtropical biodiversity in South America. Yet, over the past three decades, the region has experienced sustained deforestation and escalating forest fragmentation. This study employs multitemporal satellite imagery (1990-2020), landscape metrics (Mean Patch Area, Number of Patches, Nearest Neighbor Distance), and spatial autocorrelation (Moran's I) to assess changes in forest structure and connectivity across 1.1 million hectares. Results reveal a 12.98 % loss of native forest (similar to 129,000 ha), with deforestation peaking at 0.73 % annually (2000-2005). Fragmentation intensified as Mean Patch Area (MPA) declined by 24.6 % (from 285 to 215 ha), the number of patches (NP) more than doubled, and patch isolation (NND) increased from 95 m to 246 m. Under Argentina's Native Forest Law (OTBN), strictly protected Category I areas maintained greater structural integrity. In contrast, Categories II (regulated use) and III (low protection) exhibited severe fragmentation; by 2020, no unfragmented forest remained in Category III, and Category II displayed strong spatial coupling between deforestation and fragmentation (Moran's I > 0.5). While core protected areas remained resilient, degradation in surrounding private lands and buffer zones indicates that zoning alone cannot halt habitat loss. These findings emphasize the need to embed ecological connectivity into land-use planning and conservation frameworks. We recommend reinforcing habitat corridors, restoring degraded forests in vulnerable zones, and strengthening the enforcement of sustainable land-use regulations. This integrative approach demonstrates the utility of spatial analysis and remote sensing in informing effective conservation strategies to safeguard landscape connectivity in one of the world's most threatened forest ecosystems.

Maize (Zea mays L.) is Argentina’s second most exported crop, predominantly rainfed and cultivated across a broad range of agro-ecological zones. Given the strong sensitivity of maize yields to climatic variability, this study explores the predictive power of large-scale climate modes on maize productivity at the county level over a 30-year period (1994–2024). We analyzed four climate indices—ONI (Oceanic Niño Index), IOD (Indian Ocean Dipole), AAO (Antarctic Oscillation), and TSA (Tropical South Atlantic Index)—and their monthly correlation with detrended maize yield anomalies across 171 counties (22–39° latitude South). Based on the strength and significance of these correlations, we developed an Empirical Predictive Value (EPV) for each county by weighing the most informative index-month combinations. We then evaluated the performance of the EPV through categorical comparisons with observed yield terciles using confusion matrices and chi-squared tests. Results show that ONI exhibited the strongest and most widespread correlations, particularly for central-eastern Argentina during critical maize growth stages. IOD and AAO showed moderate but regionally relevant signals, while TSA presented a limited predictive capacity. EPV-based classifications significantly aligned with observed yield anomalies in over 40

Most crop yield forecast models operate at coarse scales (e.g., county or region) or need extensive input data for finer resolutions. Here, we present maize ( Zea mays L.) yield forecast models that require minimal user data and operate at field and regional scales throughout the growing season. Using 1853 maize field‐years in Argentina, with known location, sowing date, and yield, our models leveraged absorbed radiation (from satellite imagery), temperature‐based phenology, regional site‐year properties, El Niño‐Southern Oscillation (ENSO) phase predictions, and sowing period. At the field scale, our models achieved high accuracy at physiological maturity, with a mean error of 1 t ha −1 (16%). Yield forecasts were mainly driven by absorbed radiation during the reproductive phase and a regional factor. Early‐season forecasts incorporated ENSO and sowing period, but with reduced accuracy. When scaled to regional forecasts, the models performed even better, with a mean error of 0.3 t ha −1 (4%). These results combine a novel case of yield forecast because of the low data requirements from users, high anticipation (30–90 days before harvest), and good levels of accuracy at both field and regional scales. Additionally, the models’ interpretability makes them valuable diagnostic tools for post‐season analysis.

Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. Globe-LFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfire behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader field of environmental and ecological research.

Abstract Floods in ideal landscapes follow a coherent pattern where single water‐covered areas expand and afterward recede following the inverse sequence, but deviate in real landscapes due to natural or human factors resulting in water coverage shifts. Using remote sensing, we introduced two indices to describe the discrepancies between spatially integrated versus pixel‐level frequency distributions under maximum inundated conditions (dext) and throughout all flooding conditions (dtot), expressed as the relative weight of shifts on each landscape's maximum registered coverage, theoretically ranging between no displacement (<20%) to maximum displacement (≪inf). Globally, over 36 years inundations exhibited redistributions representing, on average, 25% and 45% of their peak extents revealing previously unnoticed extra engaged areas and rotational movements within events, rising up to 500% in meandering rivers (South America) and irrigated croplands (Central Asia). We also assessed the influence of natural and human variables and discussed the indices' potential for advancing flood research.

Real evapotranspiration (ETR) is a key variable in socio-ecological systems since it is related to the food supply, climate regulation, among others. Additionally, ETR plays a significant role in determining water yield (WY) at the catchment level, which directly impacts water availability for consumption and irrigation. Therefore, it is essential to quantify ETR and WY fluctuations in response to various human pressures to enable comprehensive water planning. In recent decades, remote sensing has become increasingly employed worldwide for hydrological monitoring and estimating ETR. In Uruguay, several approaches have been attempted to quantify ETR. However, there is still a lack of assessments concerning the performance of different products, particularly those using remote sensing. The main objectives of this article were twofold: a) to evaluate the performance of various spatial explicit approaches for estimating real ETR and b) to estimate and analyse the variability in WY derived from the different ETR products for three climatically contrasting years. To achieve these objectives, we utilized four remote sensing ETR products: the Penman-Monteith-Leuning model (PMLv2), the MODIS product, the Simplified Jackson Model based on Landsat images and INTA-SEPA model based on NOAA-AVHRR images. We also employed two water balance models at two scales: national (derived from the National Institute for Agricultural Research of Uruguay, INIA) and micro-watershed level. Our results indicate that MODIS and PMLv2 remote sensing products exhibited better performances compared to the other approaches. These products provided the highest spatial (500 m) and temporal (8 days) resolution, effectively capturing seasonal differences between land-covers. Moreover, they showed positive and strong correlations with annual precipitation and productivity. The discrepancies observed between products have direct implications on the estimation of WY, not only in terms of quantity but also in terms of spatial patterns. Future studies should explore the application of MODIS and PMLv2 ETR estimations for understanding hydrological and ecological processes, conducting climate change research, detecting and mitigating agricultural drought, and managing water resources effectively.

Los incendios son fenómenos catastróficos, devastadores, peligrosos y costosos para una región. A pesar de su relevancia, se conoce poco sobre su dinámica espacial y temporal en la provincia de La Pampa. El objetivo general de este trabajo fue identificar y caracterizar los incendios —en particular, los más extensos— ocurridos en la provincia de La Pampa durante el período julio 2001-junio 2017 (16 campañas) empleando información provista por sensores remotos. Para ello se utilizó información de focos de calor de MODIS Rapid Response distribuidos por el sistema web FIRMS. Combinando información de índices espectrales (NBR), datos de precipitación y tipos de vegetación se estudió la influencia de los factores predisponentes sobre la ocurrencia de estos eventos. Se observó que, en el período analizado, se quemaron entre 21200 y 667500 ha/año, con un tamaño promedio de evento de 708 ha. Sin embargo, en las últimas cuatro campañas se registró un incremento en la superficie total quemada y en el número de eventos ≥5000 ha. En la última campaña, además de registrarse la mayor superficie quemada (667500 ha), ocurrió la mayor cantidad de eventos (10) ≥10000 ha (50% de los eventos totales de esa categoría). La superficie quemada total en una campaña se relacionó positivamente con las precipitaciones de la campaña previa (R2=0.76, P<0.001). La vegetación más afectada fue el bosque xérico, seguido en menor medida por los bosques de algarrobo y las estepas y matorrales psamófilos. Se espera que la caracterización de la dinámica espacial y temporal de incendios mediante el uso de sensores contribuya a diseñar sistemas de prevención, alerta temprana y control en la región.

Precipitation is a key variable in different studies and applications. However, its high tempo-spatial variability makes it difficult to estimate in large areas. Remote sensors arise as an alternative to provide explicit spatial information about precipitation events at different scales. In this work, we evaluated the performance of CHIRPS precipitation in the province of Córdoba, and we proposed a correction technique through weather station data. For this purpose, we accumulated observed and satellite-based daily precipitation data in 10-days and monthly periods. Through linear regression, multiplication coefficients, intercepts, and residues were obtained, which were subsequently applied to the uncorrected CHIRPS image. The results showed that CHIRPS estimates precipitation better as time integration increases. There was also a tendency to overestimate low precipitation values and an underestimation of high values, in all periods. In the final product validation, the corrected image showed a higher correlation and errors were reduced by 30-60% compared to the uncorrected CHIRPS image. In addition, this work demonstrated that in the proposed methodology to correct the CHIRPS database the spatial correlation decreases and the error increases as the number of stations used in the correction also decreases. This showed the sensitivity of the correction technique to the distribution of meteorological stations. Therefore, it is recommended to set up measurement networks with densities equal to or greater than one station per 200000 hectares, for example. This would allow the local variability of precipitation to be captured and corrected for over and underestimation of precipitation values by the CHIRPS satellite. We concluded that the proposed methodology allows for obtaining an adjusted product that represents spatially the precipitations.

Until recently, the development of a global geography of floods was challenged by the fragmentation and heterogeneity of in situ data and the high costs of processing large amounts of remote sensing data. Such geography would facilitate the exploration of large-scale drivers of flood extent and timing including wide latitudinal, climate, and topographic effects. Here we used a monthly dataset spanning 30 years (Global Surface Water Extent) to develop a worldwide geographical characterization of slow floods (1-degree grid), weighting the relative contribution of seasonal, interannual, and long-term fluctuations on overall variability, and quantifying precipitation-flooding delays where seasonality dominated. We explored the dominance of different flooding timings across five Köppen-Geiger main climates and seven topography classes derived from modeled water table depths (i.e., hydro-topography) to contribute top-down insight about the outstanding, cross-regional flooding patterns and their likely large-scale drivers. Our results showed that, globally, the mean extent of floods averaged 0.48% of the global land area, predominantly associated with hydro-topography (>2x more extensive in flatter landscapes). Climate drove flood timings, with predictable, seasonally-dominated fluctuations in cold regions, interannual and mixed patterns in temperate climates, and more irregular (higher variability) and unpredictable (less seasonal) patterns in arid regions. Net gains of flooded area dominated temporal variability in 9% of the cells including boreal clusters likely affected by warming trends. We propose that this new geographical perspective of floods can aid different avenues of hydrological research in the upscaling and extrapolation of field studies and the parsimonious representation of floods in hydroclimatic models.

About half of the applied nitrogen (N) is not consumed by crops, causing environmental and economic costs. This N can be lost as ammonia (NH 3 ) volatilization, nitrous oxide (N 2 O) emission or leaching, among others. This work aimed to compare the amount of gaseous N losses using three different fertilizers on two consecutive experiments: one summer crop (maize) and one winter crop (wheat) in the Rolling Pampa, Argentina. The fertilizers used were urea ammonium nitrate (UAN), calcium ammonium nitrate (CAN) and AN+DMPP (ammonium nitrate‐based NPK fertilizer with DMPP nitrification inhibitor). NH 3 emissions were estimated using a semi open‐static absorption system during the first month after fertilization for each experiment. N 2 O emissions were estimated using vented static chambers during the growing season of each crop. Results show that CAN or AN+DMPP fertilizers used instead of UAN helped to reduce NH 3 volatilization by 45–50% and 62–63% on maize and wheat experiments respectively, but failed to reduce N 2 O emissions. In addition, contrary to the expected, AN+DMPP increased N 2 O emissions during the maize experiment. The majority of the gaseous N losses occurred at specific moments of the crop cycle (after N fertilization and around leaf senescence). Losses as NH 3 volatilization were higher than N 2 O emissions in the maize experiment, as expected because of the warmer temperature during this summer crop. However, N 2 O emissions were higher during the wheat crop, emphasizing the importance of factors such as meteorological conditions, previous land‐use, residual soil nitrate and stubble quality on the soil.

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9.

Forests have resistance that allows them to resist fires without changing to another state, and resilience that allows them to recover after disturbance. These properties are determined by many structural and functional determinants that interact between them. Despite the importance of structural resistance and functional resilience to wildland fires, few studies have evaluated the combined effect that structural and functional determinants have on them. Our goal was to assess the structural resistance and functional resilience to fire using remote sensing information. We specifically assessed the combined effect of pre-fire vegetation characteristics, burn severity, and post-fire precipitation on forest structural resistance and functional resilience to fire. Eighty-five forest plots of 250 m x 250 m were selected in areas that burned in 2003. For each burned plot, a paired unburned control plot of 250 m x 250 m was selected outside the burned areas. We measured burn severity and post-fire precipitations (2004-2011). We analysed MODIS time series in order to calculate the following pre- (2002) and post-fire (2011) phenological parameters: minimum level of photosynthetic activity per year; maximum level of photosynthetic activity per year; length of growing season per year; integral of annual photosynthetic activity; relative seasonality of photosynthetic activity. Also we detected plots that changed into a shrubland eight years after the fire. Fifty three per cent of burned plots changed from forest into a shrubland state. Results show that the forest structural resistance to fire depends on the balance between the level of severity and the parameters related to pre-fire aboveground net primary production. The impact of pre-fire vegetation characteristics on functional resilience ability was driven by burn severity and it's interactions with pre-fire productivity and seasonality. Results suggest that changes in forest species composition and aboveground net primary production reduced forest structural resistance and functional resilience to fire.

La interpolación espacial de las observaciones puntuales provenientes de estaciones meteorológicas es una manera recurrente de estimar la precipitación en un determinado sitio. Sin embargo, a medida que se incrementa la distancia al sitio puntual de medición estas estimaciones suelen diferir fuertemente en la cantidad de agua que realmente precipita. Los sensores remotos ópticos a bordo de satélites permiten incrementar la extensión espacial con una adecuada resolución temporal, sin embargo, presentan una baja resolución espacial. Los radares meteorológicos terrestres, por su parte, presentan mayor resolución espacial y temporal, aunque trabajos previos han demostrado un importante desfasaje entre los valores de precipitación estimados y los registrados en pluviómetros. Dada la limitada información puntual de precipitación y su variable distribución en el territorio, la identificación de campos de precipitación (es decir, el área donde efectivamente ocurre el evento precipitante) mediante los radares puede ser una importante alternativa para incrementar la precisión de las estimaciones basadas en interpolaciones convencionales. El objetivo de este trabajo fue delimitar los campos de precipitación a partir de la utilización de la red de radares meteorológicos del INTA con el fin de complementar las estimaciones de lluvia en grandes extensiones del territorio basadas en interpolaciones de datos puntuales. Para ello se consideró un período de análisis que incluyó los meses de octubre y noviembre de 2016 y un total de 8784 imágenes. Se realizó la interpolación espacial de los registros pluviométricos diarios medidos en estaciones meteorológicas y se utilizaron los radares meteorológicos para reconocer la distribución espacial de los eventos de precipitación, calculándose un umbral de detección de precipitación de 3,125 mm. Se delimitaron campos de precipitación y se generó el producto de “Interpolación Espacial utilizando Radar (IER)” a escala diaria y mensual. Se observó que solo en el 55% del área estudiada efectivamente acontecieron eventos de precipitación diaria, por lo que las interpolaciones espaciales convencionales generan una sobreestimación en el área de ocurrencia de este evento. Esto generaría una sobreestimación promedio del agua precipitada en el área de estudio de 91,1 mm y 35,2 mm para los meses de octubre y noviembre respectivamente, lo que podría afectar seriamente la toma de decisiones relacionadas con este recurso. El radar resultó ser una herramienta práctica y complementaria para la delimitación de aquellas zonas en las que se produjo el evento de precipitación y que resultan sobreestimadas por el registro discontinuo en las interpolaciones espaciales, aun más en zonas alejadas de las estaciones meteorológicas.

Validation is mandatory to quantify the reliability of satellite biophysical products that are now routinely generated by a range of sensors. This paper presents the VALERI project dedicated to the validation of the products derived from medium resolution satellite sensors (www. avignon.inra.fr/valeri/). It describes the sites used, and the methodology developed to get the high spatial resolution map of the biophysical variables considered, i.e. LAI , fAPAR and fCover that can be estimated from ground level gap fraction measurements. Sites were selected to represent , with the other validation projects, the large variation of biomes and conditions observed over the Earth’s surface. Each site is about 3 × 3 km² in size and should be flat and relatively homogeneous at the medium resolution scale. For each site, the methodology used to generate the high spatial resolution biophysical variable maps is described. It is mainly based on concurent use of local gound measurements and a high spatial resolution satellite image, generally SPOT-HRV. Local ground measurements should be representative of an elementary sampling unit (ESU) that has approximately the same size as a SPOT-HRV pixel. The ground measurements mainly consist of gap fraction measurements achieved with LAI-2000 or hemispherical photographs. The ESUs are selected over the whole 3 × 3 km² site in order to sample the range of vegetation types observed. A transfer function is subsequently established over the ESUs to relate the ground measurements of the biophysical variables considered to the correspodonding high spatial resolution satellite image data. Finally, co-kriging is applied to generate the high spatial resolution map of the biophysical variables over the 3 × 3 km² area. The methodology presented in this paper can serve as a basis for validating medium resolution satellite products. These methodological aspects are discussed and conclusions drawn on the limitations and prospects of beforementioned validation activity.

A prominent goal of policies mitigating climate change and biodiversity loss is to achieve zero deforestation in the global supply chain of key commodities, such as palm oil and soybean. However, the extent and dynamics of deforestation driven by commodity expansion are largely unknown. Here we mapped annual soybean expansion in South America between 2000 and 2019 by combining satellite observations and sample field data. From 2000 to 2019, the area cultivated with soybean more than doubled from 26.4 Mha to 55.1 Mha. Most soybean expansion occurred on pastures originally converted from natural vegetation for cattle production. The most rapid expansion occurred in the Brazilian Amazon, where soybean area increased more than tenfold, from 0.4 Mha to 4.6 Mha. Across the continent, 9% of forest loss was converted to soybean by 2016. Soybean-driven deforestation was concentrated at the active frontiers, nearly half located in the Brazilian Cerrado. Efforts to limit future deforestation must consider how soybean expansion may drive deforestation indirectly by displacing pasture or other land uses. Holistic approaches that track land use across all commodities coupled with vegetation monitoring are required to maintain critical ecosystem services. Deforestation is often driven by land conversion for growing commodity crops. This study finds that, between 2000 and 2019, most soybean expansion in South America was on pastures converted originally for cattle production, especially in the Brazilian Amazon. More soy-driven deforestation occurred in the Brazilian Cerrado.

Distinguishing between natural forests from exotic tree plantations is essential to get an accurate picture of the world’s state of forests. Most exotic tree plantations support lower levels of biodiversity and have less potential for ecosystem services supply than natural forests, and differencing them is still a challenge using standard tools. We use a novel approach in south-central of Chile to differentiate tree cover dynamics among natural forests and exotic tree plantations. Chile has one of the world’s most competitive forestry industry and the region is a global biodiversity hotspot. Our collaborative visual interpretation method combined a global database of tree cover change, remote sensing from high-resolution satellite images and expert knowledge. By distinguishing exotic tree plantation and natural forest loss, we fit spatially explicit models to estimate tree-cover loss across 40 millions of ha between 2000 and 2016. We were able to distinguish natural forests from exotic tree plantations with an overall accuracy of 99% and predicted forest loss. Total tree cover loss was continuous over time, and the disaggregation revealed that 1 549 909 ha of tree plantations were lost (mean = 96 869 ha year−1), while 206 142 ha corresponded to natural forest loss (mean = 12 884 ha year−1). Mostly of tree plantations lost returned to be plantation (51%). Natural forests were converted mainly (75%) to transitional land covers (e.g. shrubland, bare land, grassland), and an important proportion of these may finish as tree plantation. This replacement may undermine objectives of increasedcarbon storage and biodiversity. Tree planting as a solution has gained increased attention in recen years with ambitious commitments to mitigate the effects of climate change. However, negative outcomes for the environment could result if strategies incentivize the replacement of natural forests into other land covers. Initiatives to reduce carbon emissions should encourage differentiating natural forests from exotic tree plantations and pay more attention on protecting and managing sustainably the former.

Deforestation is widely studied throughout the world. However, a less evident issue is the effect of climate change and drought on remnants of native forests. The objective of this work was to understand the geographic variations in resistance to drought of the Mediterranean sclerophyllous forests of central Chile. These forests have been historically reduced and fragmented and in recent years were subjected to the most prolonged drought occurred between 2010 and 2017. Using data from the MODIS satellite sensor, temporal trends in the NDVI (Normalized Difference Vegetation Index) were quantified. We related these trends with different environmental variables to understand the effects of geographical variation and forest type as indicators of resistance to drought. We observed a significant direct effect of drought, attributable to the reduced precipitation in central Chile, and a significantly reduced NDVI in near one-third of the region forests (browning). However, NDVI and therefore forest productivity were more stable in some mesic sites such as ravine bottoms, but not on south-facing slopes. This suggests that under a regime of reduced precipitations, a greater available soil humidity would be a more important factor than the fact of receiving less solar radiation. Finally, the highest degree of browning was observed in semi-arid sclerophyllous forest dominated by species tolerant to drought. Our findings emphasize the need to consider topographic site conditions to adequately assess forest productivity and vulnerability where local wet conditions could provide drought refuges. This recent drought may be analogous to forecasted warmer and drier climate conditions with more frequent and severe droughts, so our results may serve as a general framework for climate-smart decisions in highly threatened forest restoration and conservation.

Los humedales están entre los ecosistemas más productivos y, a su vez, están fuertemente alterados por el ser humano. Los múltiples servicios que proveen dependen en gran medida del flujo de agua. Por ello, para desarrollar un plan de uso de la tierra que permita un uso productivo manteniendo la integridad ecológica es fundamental comprender los patrones de inundación. En ese sentido, la capacidad de los sensores remotos de estimar la cobertura de agua en áreas grandes a escalas espaciales y temporales detalladas contribuirían a desarrollar herramientas que favorezcan la toma de decisiones. Sin embargo, la variación temporal y espacial de los componentes del agua puede alterar sus propiedades espectrales. Se estudió la capacidad de diferentes índices espectrales derivados del sensor MODIS para estimar la cobertura de agua o la presencia/ausencia de agua. La región de estudio fue el Delta del Río Paraná, un humedal de 2 millones de hectáreas. Entre todos los modelos evaluados, uno basado en el índice espectral NDWI1 ((Rojo - SWIR) / (Rojo + SWIR)) fue el más preciso. Un valor umbral de NDWI1 = -0,2 permitió separar píxeles con menos de 60% de cobertura de agua de aquellos con más del 60% con una precisión del 91%. Mediante este modelo se describieron los patrones de inundación de diferentes unidades de paisaje de la región durante los últimos 12 años y se clasificó la región de acuerdo al impacto de los eventos de inundación ordinarios y extraordinarios. Consideramos que esta información puede ayudar a mejorar el conocimiento sobre la hidrodinámica, monitorear el impacto de algunas actividades y desarrollar una planificación regional más sostenible.

Capture of radiation by crop canopies drives growth rate, grain set, and yield. Since the fraction of photosynthetically active radiation absorbed by green area (fAPAR(g)) correlates with normalized difference vegetation index (NDVI), remote sensors have been used to monitor vegetation. With a 10-m spatial resolution and 5-d revisiting time, the recently launched Sentinel-2 satellite is a promising tool for fAPAR(g) monitoring. However, the available algorithm to estimate fAPAR(g) is based on simulations of canopy interception of several vegetation types and was never tested in field crops. Handheld sensors, such as GreenSeeker, are another alternative to estimate fAPAR(g). Our objectives were (a) to test the ability of indices derived from Sentinel-2 and GreenSeeker NDVI to capture fAPAR(g) of wheat (Triticum aestivum L.) crops, (b) to compare these sensors' performance against the moderate resolution imaging spectroradiometer (MODIS), and (c) to compare our Sentinel-2 model estimations with the available algorithm. In wheat fields in the southwest Argentinean Pampas, on several sampling dates, we measured fAPAR(g) with a quantum light sensor and NDVI with a GreenSeeker. We regressed fAPAR(g) measurements with vegetation indices from the different sources and selected the best models. Sentinel-2 and GreenSeeker NDVI precisely estimated fAPAR(g), with a performance similar to MODIS (p < .05; RMSD = 0.09, 0.11, and 0.08; R-2 = .89, .88, and .95, respectively). The available algorithm to estimate fAPAR(g) with Sentinel-2 yielded biased estimations, mainly in the lower range of fAPAR(g). These results suggest that simple models may provide fAPAR(g) estimations with Sentinel-2 and GreenSeeker in wheat crops with an accuracy suitable for agricultural applications.